The clean label trend: An ineffective heuristic that disserves both consumers and the food industry?
Bibliographic record
Abstract
What started around the late 2000s as the "Clean Label" (CL) trend has now become a meaningful segment of the food market, appealing to consumers who want foods made of a limited number of simple and recognizable ingredients. However, this description and tentative definitions of CL foods are vague, subject to multiple interpretations, and CL remains an informal denomination for foods, making consumers' demands and food manufacturers' offerings hardly compatible. Therefore, rather than attempting an illusory definition of CL foods, this narrative review aims to (1) show how CL appears to be a heuristic used by consumers to attempt to make safe and healthful food choices, (2) discuss how this heuristic overlooks many critical aspect of food safety and healthfulness and is consequently ineffective to guide consumers' choices, and (3) discuss the implications of the CL trend on the food chain's stakeholders and their relationships.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".